Search Results for author:
Found 6 papers, 1 papers with code
Automatic Routability Predictor Development Using Neural Architecture Search
The rise of machine learning technology inspires a boom of its applications in electronic design automation (EDA) and helps improve the degree of automation in chip designs.
Lithography Hotspot Detection via Heterogeneous Federated Learning with Local Adaptation
Moreover, the design houses are also unwilling to directly share such data with the other houses to build a unified model, which can be ineffective for the design house with unique design patterns due to data insufficiency.
The Dark Side: Security Concerns in Machine Learning for EDA
The growing IC complexity has led to a compelling need for design efficiency improvement through new electronic design automation (EDA) methodologies.
Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data
To further strengthen the results, we co-design a customized ML model FLNet and its personalization under the decentralized training scenario.
EDALearn: A Comprehensive RTL-to-Signoff EDA Benchmark for Democratized and Reproducible ML for EDA Research
The application of Machine Learning (ML) in Electronic Design Automation (EDA) for Very Large-Scale Integration (VLSI) design has garnered significant research attention.
PANDA: Architecture-Level Power Evaluation by Unifying Analytical and Machine Learning Solutions
Based on the formulation, we propose PANDA, an innovative architecture-level solution that combines the advantages of analytical and ML power models.
